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\begin{abstract}
In this assignment we build two classifiers in order to predict the polarity
of the movie reviews from the dataset. We use RapidMiner's multinomial Naive
Bayes Learner to build a Baysian classifier and RapidMiner's Fast Large Margin
with L2 SVM Primal learner to build a linear discriminative classifier. We
explore the effects of feature transformation, feature selection and feature
weighting techniques on our models built off the Movie Review Polarity Version
2.0. The best accuracy that we get are \optnb{} by Naive Bayes and \optflm{} by Linear SVM.
We also find that the features that carry the highest weights for both the models are fairly indicative of the
positive or negative class or the polarity of the dataset. 

\end{abstract}

